Analyses:

Annual avereage SWE

  • low swe 2004-2007 and 2018
  • Carrizo always has highest average SWE value, while Navajo mountain usually has the least

The annual average % contribution to the total annual average swe of all 6 mountain ranges:

  • Chuska: 0.81
  • Defiance Plateau 0.11
  • Black Mesa 0.06
  • Carrizo: 0.02
  • Navajo Mt 0.0023
  • Mt Powell 0.0049

Daily SWE variability (November - April Water Year)

Monthly Mean SWE variability (November - April Water Year)

ch_wk_graph <- graph_with_wateryear(ch_weekly, "Chuska", type = geom_col)
ch_wk_graph

All monthly mean

Correlation matrix of all monthly mean swe

- Black Mesa to Mt. Powell, Black Mesa to Defiance Plateau and Defiance Plateau and Mt Powell all have the highest correlations between each other - Least correlated are Black Mesa and Navajo Mt, Navajo Mt and Mt Powell, Navajo Mt and Defiance Plateau

Monthly Median

Monthly Anomalies (November - April Water Year)

Correlations Between locations

Weekly mean correlation November - April

Pearson’s Correlation R squared:

  • Chuska vs Black Mesa: 0.46

  • Carrizo vs Chuska: 0.63

  • Carrizo vs Black Mesa: 0.73

Chuska vs Black Mesa R squared: 0.4634808

Chuska vs Carrizo R squared: 0.631619

Black Mesa vs Carrizo R squared: 0.7250093

Month specific time series

November - April Averaged SWE values

  • Chuska and Defiance plateau frequently have the higest av swe values, navajo mt usually has the least.
  • most everything is melted by april

Total SWE for each region analyses

Timing of Max Weekly Average SWE Value

  • All of the high elevation regions have a negative slope, with Chuska having the most negative slope, except Navajo Mt & Carrizo
  • However, none of the trends are very statistically significant

SWE compared to Suzanne’s SCA

## Error in .f(.x[[i]], ...): object 'water_year' not found
## Error in eval(lhs, parent, parent): object 'ch_sca_month' not found
## Error in eval(lhs, parent, parent): object 'ch_sca_month' not found

SCA Anomaly was scaled down by a factor of 10 to improve analysis

## Error in as.data.frame(y): object 'ch_sca_month' not found
## Error in eval(lhs, parent, parent): object 'chuska_swe_sca' not found
## function (x, y, ...) 
## UseMethod("plot")
## <bytecode: 0x7f8918cb0f88>
## <environment: namespace:graphics>
  • SCA anomalies only somewhat track to SWE anomalies

Make average weekly swe dataframe for all the mountain ranges combined

  • first I am multiplying each region’s swe values by their area
  • then I will add these values up to get a total average amount of snow in the mountains for that week
  • This way when I get an average value, it’s the average amount of snow in the mountains at any one point